Common SQL Mistakes and How to Avoid Them

Stop making these errors and write cleaner, more efficient queries

12 min read | Level: Intermediate | Category: SQL

What We'll Cover

1. Using SELECT * in Production
2. Forgetting NULL Handling
3. Not Using Aliases Properly
4. Incorrect JOIN Usage
5. Missing WHERE in UPDATE/DELETE
6. Not Using Indexes
7. String vs Number Comparison
8. GROUP BY Mistakes
9. Subquery Performance Issues
10. Not Testing on Sample Data

Introduction

Even experienced data analysts make SQL mistakes. Some errors cause wrong results, while others can crash your database or slow down performance dramatically. In this guide, we'll walk through the 10 most common SQL mistakes and show you exactly how to avoid them.

Why This Matters

A single SQL mistake in production can lead to incorrect reports, data corruption, or hours of debugging. Learning to recognize these patterns will save you time and protect your data integrity.

Using SELECT * in Production

The Mistake

Using SELECT * to fetch all columns from a table might seem convenient, but it's a performance killer and maintenance nightmare.

-- BAD: Fetches all columns, even ones you don't need
SELECT *
FROM customers
WHERE country = 'India';

Why It's Bad

Performance: Transfers unnecessary data over the network
Maintenance: If table structure changes, your code may break
Clarity: Other developers don't know which columns you actually need

The Fix

-- GOOD: Only fetch columns you need
SELECT customer_id, customer_name, email, city
FROM customers
WHERE country = 'India';

Best Practice

Always explicitly list the columns you need. The only exception is during quick exploratory analysis in development.

Forgetting NULL Handling

The Mistake

NULL values in SQL behave differently than you might expect. Comparing anything to NULL returns NULL (unknown), not TRUE or FALSE.

-- BAD: This will NOT return rows where discount is NULL
SELECT * FROM orders
WHERE discount != 10;

-- BAD: This will also NOT work
SELECT * FROM orders
WHERE discount = NULL;

Why It's Bad

You might miss important data or get incorrect aggregations. NULL means "unknown" - it's not zero, empty string, or false.

The Fix

-- GOOD: Use IS NULL or IS NOT NULL
SELECT * FROM orders
WHERE discount != 10 OR discount IS NULL;

-- GOOD: Use COALESCE to handle NULLs
SELECT
order_id,
COALESCE(discount, 0) AS discount
FROM orders;

-- GOOD: Use NULLIF to create NULLs safely
SELECT
total_sales / NULLIF(total_orders, 0)
AS avg_order_value
FROM sales_summary;

Watch Out!

Aggregation functions like SUM(), AVG(), COUNT() ignore NULL values. COUNT(*) counts all rows, but COUNT(column_name) only counts non-NULL values.

Not Using Aliases Properly

The Mistake

Writing queries without table aliases makes code hard to read and maintain, especially with JOINs.

-- BAD: Confusing without aliases
SELECT
customers.customer_name,
orders.order_id,
orders.order_date,
order_items.quantity,
products.product_name
FROM customers
JOIN orders ON customers.customer_id =
orders.customer_id
JOIN order_items ON orders.order_id =
order_items.order_id
JOIN products ON
order_items.product_id = products.product_id;

The Fix

-- GOOD: Clear and readable with aliases
SELECT
c.customer_name,
o.order_id,
o.order_date,
oi.quantity,
p.product_name
FROM customers c
JOIN orders o ON c.customer_id =
o.customer_id
JOIN order_items oi ON o.order_id =
oi.order_id
JOIN products p ON oi.product_id =
p.product_id;

                        Alias Tips
                        Use meaningful abbreviations (c for customers, o for orders)
Be consistent across your queries
Always alias calculated columns with AS

                    

Incorrect JOIN Usage

The Mistake

Using the wrong JOIN type or not understanding how JOINs work leads to missing or duplicate data.

-- MISTAKE: Using INNER JOIN when you need all customers
SELECT c.customer_name, o.order_id
FROM customers c
INNER JOIN orders o ON c.customer_id =
o.customer_id;
-- This EXCLUDES customers who never placed an order!

Understanding JOINs

JOIN Type	Returns	Use When
INNER JOIN	Only matching rows from both tables	You need records that exist in both tables
LEFT JOIN	All rows from left + matching from right	You need all records from the main table
RIGHT JOIN	All rows from right + matching from left	Rarely used; use LEFT JOIN instead
FULL OUTER JOIN	All rows from both tables	You need to see unmatched records from both sides

The Fix

-- CORRECT: Use LEFT JOIN to include all customers
SELECT
c.customer_name,
COUNT(o.order_id) AS total_orders
FROM customers c
LEFT JOIN orders o ON c.customer_id =
o.customer_id
GROUP BY c.customer_id, c.customer_name;
-- Now customers with 0 orders will show up!

Missing WHERE in UPDATE/DELETE

The Mistake

This is the most dangerous SQL mistake. Forgetting the WHERE clause in UPDATE or DELETE affects ALL rows in the table.

-- DISASTER: Updates ALL customers to inactive!
UPDATE customers
SET status = 'inactive';

-- CATASTROPHE: Deletes ALL orders!
DELETE FROM orders;

This Can End Careers!

Running an UPDATE or DELETE without WHERE in production has caused data loss incidents at major companies. Always double-check before executing.

The Fix: Safety Checklist

-- STEP 1: First, run a SELECT to verify your WHERE clause
SELECT * FROM customers
WHERE last_login < '2025-01-01';

-- STEP 2: Check the count
SELECT COUNT(*) FROM customers
WHERE last_login < '2025-01-01';

-- STEP 3: Only then run the UPDATE/DELETE
UPDATE customers
SET status = 'inactive'
WHERE last_login < 
'2025-01-01';

-- BONUS: Use transactions for safety
BEGIN TRANSACTION;
UPDATE customers SET
status = 'inactive' WHERE
last_login < '2025-01-01';
-- Verify the changes, then:
COMMIT; -- or ROLLBACK if
something is wrong

Not Using Indexes

The Mistake

Querying large tables without proper indexes makes queries run slowly - sometimes taking minutes instead of milliseconds.

-- SLOW: Full table scan on 10 million rows
SELECT * FROM orders
WHERE customer_email = 'john@example.com';

The Fix

-- Create an index on frequently queried columns
CREATE INDEX idx_orders_email ON
orders(customer_email);

-- For composite queries, create composite indexes
CREATE INDEX idx_orders_date_status
ON orders(order_date, status);

-- Check if your query uses indexes
EXPLAIN SELECT * FROM orders
WHERE customer_email = 'john@example.com';

                        When to Create Indexes
                        Columns used in WHERE clauses frequently
Columns used in JOIN conditions
Columns used in ORDER BY
Foreign key columns

                    

String vs Number Comparison

The Mistake

Comparing strings that contain numbers doesn't work as expected because strings are sorted alphabetically.

-- BAD: If order_id is VARCHAR, this gives wrong results
SELECT * FROM orders
WHERE order_id > '100';
-- Returns: '11', '2', '200' (alphabetical, not numerical)

-- BAD: Implicit conversion can cause issues
SELECT * FROM orders
WHERE order_id = 100; -- order_id is VARCHAR

The Fix

-- GOOD: Cast to proper data type
SELECT * FROM orders
WHERE CAST(order_id AS INT) > 100;

-- BETTER: Fix the table design
ALTER TABLE orders
MODIFY COLUMN order_id INT;

Best Practice

Always use the correct data types when designing tables. Numbers should be INT/BIGINT, not VARCHAR.

GROUP BY Mistakes

The Mistake

Including non-aggregated columns in SELECT that aren't in GROUP BY, or using WHERE instead of HAVING for aggregates.

-- ERROR: customer_name not in GROUP BY or aggregate
SELECT
customer_id,
customer_name, -- This will cause an error in strict mode!
SUM(order_total)
FROM orders
GROUP BY customer_id;

-- WRONG: WHERE can't filter aggregated results
SELECT customer_id, SUM(order_total)
AS total
FROM orders
WHERE SUM(order_total) > 1000 -- ERROR!
GROUP BY customer_id;

The Fix

-- GOOD: Include all non-aggregated columns in GROUP BY
SELECT
customer_id,
customer_name,
SUM(order_total) AS total_spent
FROM orders
GROUP BY customer_id, customer_name;

-- GOOD: Use HAVING for aggregate conditions
SELECT customer_id, SUM(order_total)
AS total
FROM orders
GROUP BY customer_id
HAVING SUM(order_total) > 1000;

WHERE	HAVING
Filters rows BEFORE grouping	Filters groups AFTER grouping
Cannot use aggregate functions	Used with aggregate functions
WHERE status = 'active'	HAVING COUNT(*) > 5

Subquery Performance Issues

The Mistake

Using correlated subqueries that run once for each row in the outer query, causing severe performance issues.

-- SLOW: Correlated subquery runs for EVERY row
SELECT
o.order_id,
o.order_total,
(SELECT AVG(order_total)
FROM orders o2
WHERE o2.customer_id = o.customer_id) AS customer_avg
FROM orders o;

The Fix

-- FAST: Use JOIN with a derived table instead
SELECT
o.order_id,
o.order_total,
ca.customer_avg
FROM orders o
JOIN (
SELECT customer_id, AVG(order_total)
AS customer_avg
FROM orders
GROUP BY customer_id
) ca ON o.customer_id = ca.customer_id;

-- EVEN BETTER: Use window functions
SELECT
order_id,
order_total,
AVG(order_total) OVER (PARTITION BY customer_id) AS
customer_avg
FROM orders;

Not Testing on Sample Data

The Mistake

Running untested queries directly on production data without verifying the logic first.

The Fix: Safe Testing Workflow

-- STEP 1: Add LIMIT to test your query logic
SELECT * FROM large_table
WHERE complex_condition
LIMIT 10;

-- STEP 2: Use COUNT to understand scale
SELECT COUNT(*) FROM large_table
WHERE complex_condition;

-- STEP 3: For UPDATE/DELETE, test in a transaction
BEGIN TRANSACTION;
UPDATE large_table SET column = value
WHERE condition;
SELECT * FROM large_table WHERE condition; -- Verify
ROLLBACK; -- Undo if testing, COMMIT if
correct

                        Pro Tips
                        Create a development/staging database with sample data
Use EXPLAIN to understand query execution plans
Always backup before running bulk UPDATE/DELETE
Have a peer review queries before production runs

                    

Quick Reference Checklist

Avoid SELECT * - list specific columns
Handle NULLs explicitly with IS NULL, COALESCE
Use meaningful table aliases
Choose the right JOIN type
ALWAYS use WHERE with UPDATE/DELETE
Create indexes for frequently queried columns
Use correct data types
Use HAVING (not WHERE) for aggregate filters
Prefer JOINs over correlated subqueries
Test queries before running on production

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